TypeSafe AI Meets Audit-Proof Finance
Ask most AI a question it can’t answer, and it answers anyway. In finance, that confident wrong answer is the expensive kind: cash applied to the wrong invoice, dunning paused for a customer who never promised to pay, a bank-detail change approved for a fraudster.
TypeSafe AI’s model, Jev, is built differently. It returns typed decisions with calibrated confidence, and when the evidence doesn’t support a call, it says so. Kognitos wraps Jev in English as Code, so the rules that decide what happens next, including how sure is sure enough, are written in plain English that business users, controllers, and auditors can read.
See how Kognitos works for you.
Speakers
More Webinars
Session FAQ
Wednesday, October 7, 2026, at 1:00 PM PT. The recording runs 60 minutes.
The full session is embedded at the top of this page and is also published on the Kognitos YouTube channel. No registration is required.
George Williams and Rushil Khurana.
Jev, TypeSafe AI's model, returns typed decisions with a calibrated confidence score instead of a fluent guess. When the evidence doesn't support a call, it says so, and the item goes to a person instead of being acted on.
It makes TypeSafe usable by finance teams. The rules that decide what happens next, including how confident Jev must be before anything runs automatically, are written in plain English that controllers and auditors can read and change, with no code or model APIs.
Two automations, end to end: cash application, and IT incident severity with GDPR and DORA regulatory clocks. Roughly half of the session is live.
It does not act. Each decision carries a confidence score, and you set the bar it has to clear. Anything below the threshold goes to a review queue with the top suggestions attached, and the reviewer's choice is recorded alongside the original decision.
Writing anything, multi-step calculation, exact rules with no judgment, and images or audio. The session covers where Jev fits and where it doesn't at 51:00.
Controllers, CFOs, AR and AP leads, and anyone evaluating decision models against a real control environment.